Solving Packing Problems by a Distributed Global Optimization Algorithm

Abstract EN

Packing optimization problems aim to seek the best way of placing a given set of rectangular boxes within a minimum volume rectangular box.

Current packing optimization methods either find it difficult to obtain an optimal solution or require too many extra 0-1 variables in the solution process.

This study develops a novel method to convert the nonlinear objective function in a packing program into an increasing function with single variable and two fixed parameters.

The original packing program then becomes a linear program promising to obtain a global optimum.

Such a linear program is decomposed into several subproblems by specifying various parameter values, which is solvable simultaneously by a distributed computation algorithm.

A reference solution obtained by applying a genetic algorithm is used as an upper bound of the optimal solution, used to reduce the entire search region.